Triple
T36612756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hagenberg |
E903511
|
entity |
| Predicate | distanceToLinzApproxKm |
P22795
|
FINISHED |
| Object | about 20 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: about 20 | Statement: [Hagenberg, distanceToLinzApproxKm, about 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLinzApproxKm Context triple: [Hagenberg, distanceToLinzApproxKm, about 20]
-
A.
distanceToLinz_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Linz.
-
B.
approximateDistanceKm
chosen
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
C.
distanceToSplitByRoad_km
Indicates the distance in kilometers from a given location to the nearest point where a road splits or branches.
-
D.
depthApproxKm
Indicates the approximate depth of something measured in kilometers.
-
E.
tourDistanceApproxKm
Indicates an approximate total distance, measured in kilometers, covered during a tour or journey.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.